Use of Machine Learning for Expanding Realistic and Usable Routes for Data Analysis on Sustainable Mobility

The current mobility or the transition to more sustainable alternatives are constantly changing. For Promoting a sustainable mobility and for investing in a proper infrastructure, we need accurate data regarding the mobility behavior. Gathering location information such as GPS can help to improve the charging infrastructure and the bicycle or pedestrian paths. This motivates the citizens to use sustainable means of transportation such as bicycles or electric cars. However, using personal information via GPS data can cause some challenges: preserving data privacy while keeping data quality to get useful analysis results. This paper presents an advanced approach of processing GPS data based on machine learning and spatial cloaking in contrast to current approaches focusing on common algorithms only. The evaluation has been conducted by generating simulated GPS trips. As a result, the presented approach provides an algorithm that prevents a complete loss of useful data while protecting the privacy of each user in cases where cloaking areas are close together.